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Proceedings in Adaptation, Learning and Optimization Ser.: Proceedings of ELM2019 by Chi Man Vong (2020, Hardcover)

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Title: Proceedings Of Elm2019. Number of Pages: 295. Language: English. Weight: 0.98 lbs. Publication Date: 2020-11-13. Publisher: SPRINGER NATURE.

About this product

Product Identifiers

Publisher
Springer International Publishing A&G
ISBN-10
3030589889
ISBN-13
9783030589882
eBay Product ID (ePID)
15050415526

Product Key Features

Number of Pages
VI, 182 Pages
Language
English
Publication Name
Proceedings of Elm2019
Subject
Engineering (General), Intelligence (Ai) & Semantics
Publication Year
2020
Type
Textbook
Author
Chi Man Vong
Subject Area
Computers, Technology & Engineering
Series
Proceedings in Adaptation, Learning and Optimization Ser.
Format
Hardcover

Dimensions

Item Weight
16 Oz
Item Length
9.3 in
Item Width
6.1 in

Additional Product Features

Series Volume Number
14
Number of Volumes
1 vol.
Illustrated
Yes
Table Of Content
Evolutionary Extreme Learning Machine Based Weighted Fuzzy-rough Nearest-neighbour Algorithm.-NNRW-based algorithm selection for software model checking.- An extreme learning machine method for diagnosis of patellofemoral pain syndrome.- Extreme Learning Machines for Signature Verification.-Website Classification from Webpage Renders.- ELM Algorithms Optimized by WOA for Motor Imagery Classification.- The Octonion Extreme Learning Machine.- Scikit-ELM: an Extreme Learning Machine toolbox for dynamic and scalable learning.- High-performance ELM for Memory Constrained Edge Computing Devices with Metal Performance Shaders.- Validating Untrained Human Annotations using Extreme Learning Machines.- ELM Feature Selection and SOM Data Visualization for Nursing Survey Dataset.
Synopsis
Evolutionary Extreme Learning Machine Based Weighted Fuzzy-rough Nearest-neighbour Algorithm.-NNRW-based algorithm selection for software model checking.- An extreme learning machine method for diagnosis of patellofemoral pain syndrome.- Extreme Learning Machines for Signature Verification.-Website Classification from Webpage Renders.- ELM Algorithms Optimized by WOA for Motor Imagery Classification.- The Octonion Extreme Learning Machine.- Scikit-ELM: an Extreme Learning Machine toolbox for dynamic and scalable learning.- High-performance ELM for Memory Constrained Edge Computing Devices with Metal Performance Shaders.- Validating Untrained Human Annotations using Extreme Learning Machines.- ELM Feature Selection and SOM Data Visualization for Nursing Survey Dataset., This book contains some selected papers from the International Conference on Extreme Learning Machine 2019, which was held in Yangzhou, China, December 14-16, 2019. Extreme Learning Machines (ELMs) aim to enable pervasive learning and pervasive intelligence. As advocated by ELM theories, it is exciting to see the convergence of machine learning and biological learning from the long-term point of view. ELM may be one of the fundamental 'learning particles' filling the gaps between machine learning and biological learning (of which activation functions are even unknown). ELM represents a suite of (machine and biological) learning techniques in which hidden neurons need not be tuned: inherited from their ancestors or randomly generated. ELM learning theories show that effective learning algorithms can be derived based on randomly generated hidden neurons (biological neurons, artificial neurons, wavelets, Fourier series, etc) as long as they are nonlinear piecewise continuous, independent of training data and application environments. Increasingly, evidence from neuroscience suggests that similar principles apply in biological learning systems. ELM theories and algorithms argue that "random hidden neurons" capture an essential aspect of biological learning mechanisms as well as the intuitive sense that the efficiency of biological learning need not rely on computing power of neurons. ELM theories thus hint at possible reasons why the brain is more intelligent and effective than current computers. The main theme of ELM2019 is Hierarchical ELM, AI for IoT, Synergy of Machine Learning and Biological Learning. This conference provides a forum for academics, researchers and engineers to share and exchange R&D experience on both theoretical studies and practical applications of the ELM technique and brain learning. This book covers theories, algorithms and applications of ELM. It gives readers a glance of the most recent advances of ELM.
LC Classification Number
Q342

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Proceedings in Adaptation, Learning and Optimization Ser.: Proceedings of ELM2019 by Chi Man Vong (2020, Hardcover)

About this product

Product Identifiers

Product Key Features

Dimensions

Additional Product Features

All listings for this product

item 1 Proceedings Of Elm2019

item 2 Proceedings of ELM2019 by Jiuwen Cao (English) Hardcover Book

item 3 Proceedings of Elm2019, Hardcover by Cao, Jiuwen (EDT); Vong, Chi Man (EDT); ...

item 4 Proceedings of Elm2019, Hardcover by Cao, Jiuwen (EDT); Vong, Chi Man (EDT); ...

Best Selling in Textbooks

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Self Heal by Design : The Role of Micro-Organisms for Health by Barbara O'Neill (2018, Trade Paperback)

Designing Data-Intensive Applications : The Big Ideas Behind Reliable, Scalable, and Maintainable Systems by Martin Kleppmann (2017, Trade Paperback)

Desk Reference to the Diagnostic Criteria from DSM-5-TR by American Psychiatric Association (2022, Trade Paperback)

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